springboot+mybatis+druid 多数据源

时间  2021-07-14 标签 springboot druid mybatis

Druid介绍和使用

在使用Druid之前，先来简单的了解下Druid。

Druid是一个数据库连接池。Druid可以说是目前最好的数据库连接池！因其优秀的功能、性能和扩展性方面，深受开发人员的青睐。
Druid已经在阿里巴巴部署了超过600个应用，经过一年多生产环境大规模部署的严苛考验。Druid是阿里巴巴开发的号称为监控而生的数据库连接池！

同时Druid不仅仅是一个数据库连接池，Druid 核心主要包括三部分：

  • 基于Filter－Chain模式的插件体系。
  • DruidDataSource 高效可管理的数据库连接池。
  • SQLParser

Druid的主要功能如下:

  1. 是一个高效、功能强大、可扩展性好的数据库连接池。
  2. 可以监控数据库访问性能。
  3. 数据库密码加密
  4. 获得SQL执行日志
  5. 扩展JDBC

介绍方面这块就不再多说，具体的可以看官方文档。
那么开始介绍Druid如何使用。

首先是Maven依赖，只需要添加druid这一个jar就行了。

<dependency>
         <groupId>com.alibaba</groupId>
         <artifactId>druid</artifactId>
         <version>1.1.8</version>
  </dependency>

配置方面，主要的只需要在application.properties或application.yml添加如下就可以了。
说明：因为这里我是用来两个数据源，所以稍微有些不同而已。Druid 配置的说明在下面中已经说的很详细了，这里我就不在说明了。

application.yml

server:
  port: 8080
  servlet:
    context-path: /equaker

spring:
  #主数据源
  master:
    datasource:
      type: com.alibaba.druid.pool.DruidDataSource
      name: master
      url: jdbc:mysql://localhost:3306/wdz?useUnicode=true&characterEncoding=utf-8
      username: root
      password: 123456
      driver-class-name: com.mysql.jdbc.Driver
 #从数据源
  cluster:
    datasource:
      type: com.alibaba.druid.pool.DruidDataSource
      name: cluster
      url: jdbc:mysql://localhost:3306/yufu?useUnicode=true&characterEncoding=utf-8
      username: root
      password: 123456
      driver-class-name: com.mysql.jdbc.Driver
  datasource:
    druid:
        # 下面为连接池的补充设置，应用到上面所有数据源中
        # 初始化大小，最小，最大
        initialSize: 5
        minIdle: 5
        maxActive: 20
        # 配置获取连接等待超时的时间
        maxWait: 60000
        # 配置间隔多久才进行一次检测，检测需要关闭的空闲连接，单位是毫秒
        timeBetweenEvictionRunsMillis: 60000
        # 配置一个连接在池中最小生存的时间，单位是毫秒
        minEvictableIdleTimeMillis: 30000
        validationQuery: SELECT 1
        validationQueryTimeout: 10000
        testWhileIdle: true
        testOnBorrow: false
        testOnReturn: false
        # 打开PSCache，并且指定每个连接上PSCache的大小
        poolPreparedStatements: true
        maxPoolPreparedStatementPerConnectionSize: 20
        filters: stat,wall
        # 通过connectProperties属性来打开mergeSql功能；慢SQL记录
        connectionProperties: druid.stat.mergeSql=true;druid.stat.slowSqlMillis=5000
        # 合并多个DruidDataSource的监控数据
        useGlobalDataSourceStat: true

mybatis:
  mapper-locations: classpath:master/*Mapper.xml
  type-aliases-package: com.equaker.model

  configuration:
    database-id: db1


#pagehelper
pagehelper:
  helperDialect: mysql
  reasonable: true
  supportMethodsArguments: true
  params: count=countSql
logging:
  level:
     com.equaker.mapper : debug

成功添加了配置文件之后，我们再来编写Druid相关的类。
首先是MasterDataSourceConfig.java这个类，这个是默认的数据源配置类。

MasterDataSourceConfig.hava

package com.equaker.druid;

import com.alibaba.druid.pool.DruidDataSource;
import org.apache.ibatis.session.SqlSessionFactory;
import org.mybatis.spring.SqlSessionFactoryBean;
import org.mybatis.spring.annotation.MapperScan;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.context.annotation.Primary;
import org.springframework.core.io.support.PathMatchingResourcePatternResolver;
import org.springframework.jdbc.datasource.DataSourceTransactionManager;

import javax.sql.DataSource;
import java.sql.SQLException;

/**
 * 主数据源
 */
@Configuration
@MapperScan(basePackages = MasterDataSourceConfig.PACKAGE, sqlSessionFactoryRef = "masterSqlSessionFactory")
public class MasterDataSourceConfig {

    static final String PACKAGE = "com.equaker.mapper.master";
    static final String MAPPER_LOCATION = "classpath:mapper/master/*.xml";

    @Value("${spring.master.datasource.url}")
    private String url;

    @Value("${spring.master.datasource.username}")
    private String username;

    @Value("${spring.master.datasource.password}")
    private String password;

    @Value("${spring.master.datasource.driver-class-name}")
    private String driverClassName;

    @Value("${spring.master.datasource.name}")
    private String name;


    @Value("${spring.datasource.druid.initialSize}")
    private int initialSize;

    @Value("${spring.datasource.druid.minIdle}")
    private int minIdle;

    @Value("${spring.datasource.druid.maxActive}")
    private int maxActive;

    @Value("${spring.datasource.druid.maxWait}")
    private int maxWait;

    @Value("${spring.datasource.druid.timeBetweenEvictionRunsMillis}")
    private int timeBetweenEvictionRunsMillis;

    @Value("${spring.datasource.druid.minEvictableIdleTimeMillis}")
    private int minEvictableIdleTimeMillis;

    @Value("${spring.datasource.druid.validationQuery}")
    private String validationQuery;

    @Value("${spring.datasource.druid.testWhileIdle}")
    private boolean testWhileIdle;

    @Value("${spring.datasource.druid.testOnBorrow}")
    private boolean testOnBorrow;

    @Value("${spring.datasource.druid.testOnReturn}")
    private boolean testOnReturn;

    @Value("${spring.datasource.druid.poolPreparedStatements}")
    private boolean poolPreparedStatements;

    @Value("${spring.datasource.druid.maxPoolPreparedStatementPerConnectionSize}")
    private int maxPoolPreparedStatementPerConnectionSize;

    @Value("${spring.datasource.druid.filters}")
    private String filters;

    @Value("{spring.datasource.druid.connectionProperties}")
    private String connectionProperties;


    @Bean(name = "masterDataSource")
    @Primary
    public DataSource masterDataSource() {
        DruidDataSource dataSource = new DruidDataSource();
        dataSource.setName(name);
        dataSource.setUrl(url);
        dataSource.setUsername(username);
        dataSource.setPassword(password);
        dataSource.setDriverClassName(driverClassName);

        //具体配置
        dataSource.setInitialSize(initialSize);
        dataSource.setMinIdle(minIdle);
        dataSource.setMaxActive(maxActive);
        dataSource.setMaxWait(maxWait);
        dataSource.setTimeBetweenEvictionRunsMillis(timeBetweenEvictionRunsMillis);
        dataSource.setMinEvictableIdleTimeMillis(minEvictableIdleTimeMillis);
        dataSource.setValidationQuery(validationQuery);
        dataSource.setTestWhileIdle(testWhileIdle);
        dataSource.setTestOnBorrow(testOnBorrow);
        dataSource.setTestOnReturn(testOnReturn);
        dataSource.setPoolPreparedStatements(poolPreparedStatements);
        dataSource.setMaxPoolPreparedStatementPerConnectionSize(maxPoolPreparedStatementPerConnectionSize);
        try {
            dataSource.setFilters(filters);
        } catch (SQLException e) {
            e.printStackTrace();
        }
        dataSource.setConnectionProperties(connectionProperties);
        return dataSource;
    }

    @Bean(name = "masterTransactionManager")
    @Primary
    public DataSourceTransactionManager masterTransactionManager() {
        return new DataSourceTransactionManager(masterDataSource());
    }

    @Bean(name = "masterSqlSessionFactory")
    @Primary
    public SqlSessionFactory masterSqlSessionFactory(@Qualifier("masterDataSource") DataSource masterDataSource)
            throws Exception {
        final SqlSessionFactoryBean sessionFactory = new SqlSessionFactoryBean();
        sessionFactory.setDataSource(masterDataSource);
        sessionFactory.setMapperLocations(new PathMatchingResourcePatternResolver()
                .getResources(MasterDataSourceConfig.MAPPER_LOCATION));
        return sessionFactory.getObject();
    }
}
ClusterDataSourceConfig.java
package com.equaker.druid;

import com.alibaba.druid.pool.DruidDataSource;
import org.apache.ibatis.session.SqlSessionFactory;
import org.mybatis.spring.SqlSessionFactoryBean;
import org.mybatis.spring.annotation.MapperScan;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.context.annotation.Primary;
import org.springframework.core.io.support.PathMatchingResourcePatternResolver;
import org.springframework.jdbc.datasource.DataSourceTransactionManager;

import javax.sql.DataSource;
import java.sql.SQLException;

/**
 * 主数据源
 */
@Configuration
@MapperScan(basePackages = ClusterDataSourceConfig.PACKAGE, sqlSessionFactoryRef = "clusterSqlSessionFactory")
public class ClusterDataSourceConfig {

    static final String PACKAGE = "com.equaker.mapper.cluster";
    static final String MAPPER_LOCATION = "classpath:mapper/cluster/*.xml";

    @Value("${spring.cluster.datasource.url}")
    private String url;

    @Value("${spring.cluster.datasource.username}")
    private String username;

    @Value("${spring.cluster.datasource.password}")
    private String password;

    @Value("${spring.cluster.datasource.driver-class-name}")
    private String driverClassName;

    @Value("${spring.cluster.datasource.name}")
    private String name;



    @Value("${spring.datasource.druid.initialSize}")
    private int initialSize;

    @Value("${spring.datasource.druid.minIdle}")
    private int minIdle;

    @Value("${spring.datasource.druid.maxActive}")
    private int maxActive;

    @Value("${spring.datasource.druid.maxWait}")
    private int maxWait;

    @Value("${spring.datasource.druid.timeBetweenEvictionRunsMillis}")
    private int timeBetweenEvictionRunsMillis;

    @Value("${spring.datasource.druid.minEvictableIdleTimeMillis}")
    private int minEvictableIdleTimeMillis;

    @Value("${spring.datasource.druid.validationQuery}")
    private String validationQuery;

    @Value("${spring.datasource.druid.testWhileIdle}")
    private boolean testWhileIdle;

    @Value("${spring.datasource.druid.testOnBorrow}")
    private boolean testOnBorrow;

    @Value("${spring.datasource.druid.testOnReturn}")
    private boolean testOnReturn;

    @Value("${spring.datasource.druid.poolPreparedStatements}")
    private boolean poolPreparedStatements;

    @Value("${spring.datasource.druid.maxPoolPreparedStatementPerConnectionSize}")
    private int maxPoolPreparedStatementPerConnectionSize;

    @Value("${spring.datasource.druid.filters}")
    private String filters;

    @Value("{spring.datasource.druid.connectionProperties}")
    private String connectionProperties;


    @Bean(name = "clusterDataSource")
    public DataSource masterDataSource() {
        DruidDataSource dataSource = new DruidDataSource();
        dataSource.setName(name);
        dataSource.setUrl(url);
        dataSource.setUsername(username);
        dataSource.setPassword(password);
        dataSource.setDriverClassName(driverClassName);

        //具体配置
        dataSource.setInitialSize(initialSize);
        dataSource.setMinIdle(minIdle);
        dataSource.setMaxActive(maxActive);
        dataSource.setMaxWait(maxWait);
        dataSource.setTimeBetweenEvictionRunsMillis(timeBetweenEvictionRunsMillis);
        dataSource.setMinEvictableIdleTimeMillis(minEvictableIdleTimeMillis);
        dataSource.setValidationQuery(validationQuery);
        dataSource.setTestWhileIdle(testWhileIdle);
        dataSource.setTestOnBorrow(testOnBorrow);
        dataSource.setTestOnReturn(testOnReturn);
        dataSource.setPoolPreparedStatements(poolPreparedStatements);
        dataSource.setMaxPoolPreparedStatementPerConnectionSize(maxPoolPreparedStatementPerConnectionSize);
        try {
            dataSource.setFilters(filters);
        } catch (SQLException e) {
            e.printStackTrace();
        }
        dataSource.setConnectionProperties(connectionProperties);
        return dataSource;
    }

    @Bean(name = "clusterTransactionManager")
    public DataSourceTransactionManager masterTransactionManager() {
        return new DataSourceTransactionManager(masterDataSource());
    }

    @Bean(name = "clusterSqlSessionFactory")
    public SqlSessionFactory masterSqlSessionFactory(@Qualifier("clusterDataSource") DataSource masterDataSource)
            throws Exception {
        final SqlSessionFactoryBean sessionFactory = new SqlSessionFactoryBean();
        sessionFactory.setDataSource(masterDataSource);
        sessionFactory.setMapperLocations(new PathMatchingResourcePatternResolver()
                .getResources(ClusterDataSourceConfig.MAPPER_LOCATION));
        return sessionFactory.getObject();
    }
}

其中这两个注解说明下:

  • @Primary ：标志这个 Bean 如果在多个同类 Bean 候选时，该 Bean
    优先被考虑。多数据源配置的时候注意，必须要有一个主数据源，用 @Primary 标志该 Bean。

  • @MapperScan： 扫描 Mapper 接口并容器管理。

需要注意的是sqlSessionFactoryRef 表示定义一个唯一 SqlSessionFactory 实例。

上面的配置完之后，就可以将Druid作为连接池使用了。但是Druid并不简简单单的是个连接池，它也可以说是一个监控应用，它自带了web监控界面，可以很清晰的看到SQL相关信息。
在SpringBoot中运用Druid的监控作用，只需要编写StatViewServlet和WebStatFilter类，实现注册服务和过滤规则。这里我们可以将这两个写在一起，使用@Configuration和@Bean。
为了方便理解，相关的配置说明也写在代码中了，这里就不再过多赘述了。
代码如下:

DruidConfiguration.java
package com.equaker.druid;

import com.alibaba.druid.support.http.StatViewServlet;
import com.alibaba.druid.support.http.WebStatFilter;
import org.springframework.boot.web.servlet.FilterRegistrationBean;
import org.springframework.boot.web.servlet.ServletRegistrationBean;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

/**
 * druid数据源监控web应用
 */
@Configuration
public class DruidConfiguration {

    @Bean
    public ServletRegistrationBean druidStatViewServle() {
        //注册服务
        ServletRegistrationBean servletRegistrationBean = new ServletRegistrationBean(
                new StatViewServlet(), "/druid/*");
        // 白名单(为空表示,所有的都可以访问,多个IP的时候用逗号隔开)
        servletRegistrationBean.addInitParameter("allow", "127.0.0.1");
        // IP黑名单 (存在共同时，deny优先于allow)
        servletRegistrationBean.addInitParameter("deny", "127.0.0.2");
        // 设置登录的用户名和密码
        servletRegistrationBean.addInitParameter("loginUsername", "equaker");
        servletRegistrationBean.addInitParameter("loginPassword", "123456");
        // 是否能够重置数据.
        servletRegistrationBean.addInitParameter("resetEnable", "false");
        return servletRegistrationBean;
    }

    @Bean
    public FilterRegistrationBean druidStatFilter() {
        FilterRegistrationBean filterRegistrationBean = new FilterRegistrationBean(
                new WebStatFilter());
        // 添加过滤规则
        filterRegistrationBean.addUrlPatterns("/*");
        // 添加不需要忽略的格式信息
        filterRegistrationBean.addInitParameter("exclusions",
                "*.js,*.gif,*.jpg,*.png,*.css,*.ico,/druid/*");
        System.out.println("druid初始化成功!");
        return filterRegistrationBean;

    }

}

 编写完之后，启动程序，在浏览器输入:http://127.0.0.1:8084/druid/index.html ，然后输入设置的用户名和密码，便可以访问Web界面了。

效果如图：

 

DemoController.java:

package com.equaker.controller;

import com.equaker.mapper.cluster.UserMapper;
import com.equaker.mapper.master.EQMapper;
import com.equaker.model.EQ;
import com.equaker.model.User;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Controller;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.ResponseBody;

import java.math.BigDecimal;
import java.util.Date;
import java.util.HashMap;
import java.util.Map;

@Controller
@RequestMapping("demo")
public class DemoController {

    @Autowired
    private EQMapper eqMapper;
    @Autowired
    private UserMapper userMapper;

    @RequestMapping("/save")
    @ResponseBody
    public EQ save(){

        EQ eq = new EQ();
        eq.setcAmount(new BigDecimal(10));
        eq.setcCardno("1");
        eq.setcDatetime(new Date());
        eq.setcGcode("1");
        eq.setcGname("sdq");
        eq.setcId("1");
        eq.setcQtty(new BigDecimal(10));
        eq.setcSname("ss");
        eq.setcStoreId("1");
        eqMapper.insert(eq);
        return eq;
    }

    @RequestMapping("/saveUser")
    @ResponseBody
    public User saveUser(){

        User user = new User();
        user.setName("sdq");
        user.setAge(23);
        user.setCreateTime(new Date());
        user.setUpdateTime(new Date());

        userMapper.save(user);
        return user;
    }

}

 访问：http://localhost:8080/equaker/demo/save 使用master数据源 ok

 访问：http://localhost:8080/equaker/demo/saveUser 使用cluster数据源 ok

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